Analysing protein dynamics using machine learning based generative models

The ability to understand and model proteins’ dynamics is of great relevance in biology and medicine. A good comprehension of the way the proteins change their structure is important as these transitions give the function of the protein within the organism. In this paper we are introducing an unsupervised learning based approach using variational autoencoders, for uncovering protein motions and conformational transitions. The main goal of the research is to offer an interpretable method for proteins’ trajectories visualisation by learning a low dimensional space that accurately represents the input data, as empirically confirmed through the performed experiments. An additional aim is to comparatively evaluate the impact of two protein representations on the learning process.